diff --git a/lib/crewai/src/crewai/__init__.py b/lib/crewai/src/crewai/__init__.py
index e82b92511..ab47aa634 100644
--- a/lib/crewai/src/crewai/__init__.py
+++ b/lib/crewai/src/crewai/__init__.py
@@ -81,6 +81,7 @@ _track_install_async()
 
 _LAZY_IMPORTS: dict[str, tuple[str, str]] = {
     "Memory": ("crewai.memory.unified_memory", "Memory"),
+    "MemoryPromptConfig": ("crewai.memory.types", "MemoryPromptConfig"),
 }
 
 
@@ -210,6 +211,7 @@ __all__ = [
     "Knowledge",
     "LLMGuardrail",
     "Memory",
+    "MemoryPromptConfig",
     "PlanningConfig",
     "Process",
     "RuntimeState",
diff --git a/lib/crewai/src/crewai/memory/analyze.py b/lib/crewai/src/crewai/memory/analyze.py
index e700f4281..2709c57fc 100644
--- a/lib/crewai/src/crewai/memory/analyze.py
+++ b/lib/crewai/src/crewai/memory/analyze.py
@@ -8,7 +8,7 @@ from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field
 
-from crewai.memory.types import MemoryRecord, ScopeInfo
+from crewai.memory.types import MemoryPromptConfig, MemoryRecord, ScopeInfo
 from crewai.utilities.i18n import get_i18n
 
 
@@ -140,19 +140,23 @@ class ConsolidationPlan(BaseModel):
     )
 
 
-def _get_prompt(key: str) -> str:
-    """Retrieve a memory prompt from the i18n translations.
-
-    Args:
-        key: The prompt key under the "memory" section.
-
-    Returns:
-        The prompt string.
-    """
+def _memory_prompt_line(
+    memory_prompt: MemoryPromptConfig | None,
+    key: str,
+) -> str:
+    """Resolve one memory prompt: override string or bundled translation."""
+    if memory_prompt is not None:
+        raw = getattr(memory_prompt, key, None)
+        if isinstance(raw, str) and raw.strip():
+            return raw
     return get_i18n().memory(key)
 
 
-def extract_memories_from_content(content: str, llm: Any) -> list[str]:
+def extract_memories_from_content(
+    content: str,
+    llm: Any,
+    memory_prompt: MemoryPromptConfig | None = None,
+) -> list[str]:
     """Use the LLM to extract discrete memory statements from raw content.
 
     This is a pure helper: it does NOT store anything. Callers should call
@@ -164,15 +168,21 @@ def extract_memories_from_content(content: str, llm: Any) -> list[str]:
     Args:
         content: Raw text (e.g. task description + result dump).
         llm: The LLM instance to use.
+        memory_prompt: Optional per-step prompt strings (see ``MemoryPromptConfig``).
 
     Returns:
         List of short, self-contained memory statements (or [content] on failure).
     """
     if not (content or "").strip():
         return []
-    user = _get_prompt("extract_memories_user").format(content=content)
+    user = _memory_prompt_line(memory_prompt, "extract_memories_user").format(
+        content=content
+    )
     messages = [
-        {"role": "system", "content": _get_prompt("extract_memories_system")},
+        {
+            "role": "system",
+            "content": _memory_prompt_line(memory_prompt, "extract_memories_system"),
+        },
         {"role": "user", "content": user},
     ]
     try:
@@ -202,6 +212,7 @@ def analyze_query(
     available_scopes: list[str],
     scope_info: ScopeInfo | None,
     llm: Any,
+    memory_prompt: MemoryPromptConfig | None = None,
 ) -> QueryAnalysis:
     """Use the LLM to analyze a recall query.
 
@@ -212,6 +223,7 @@ def analyze_query(
         available_scopes: Scope paths that exist in the store.
         scope_info: Optional info about the current scope.
         llm: The LLM instance to use.
+        memory_prompt: Optional per-step prompt strings.
 
     Returns:
         QueryAnalysis with keywords, suggested_scopes, complexity, recall_queries, time_filter.
@@ -219,13 +231,16 @@ def analyze_query(
     scope_desc = ""
     if scope_info:
         scope_desc = f"Current scope has {scope_info.record_count} records, categories: {scope_info.categories}"
-    user = _get_prompt("query_user").format(
+    user = _memory_prompt_line(memory_prompt, "query_user").format(
         query=query,
         available_scopes=available_scopes or ["/"],
         scope_desc=scope_desc,
     )
     messages = [
-        {"role": "system", "content": _get_prompt("query_system")},
+        {
+            "role": "system",
+            "content": _memory_prompt_line(memory_prompt, "query_system"),
+        },
         {"role": "user", "content": user},
     ]
     try:
@@ -269,6 +284,7 @@ def analyze_for_save(
     existing_scopes: list[str],
     existing_categories: list[str],
     llm: Any,
+    memory_prompt: MemoryPromptConfig | None = None,
 ) -> MemoryAnalysis:
     """Infer scope, categories, importance, and metadata for a single memory.
 
@@ -280,17 +296,21 @@ def analyze_for_save(
         existing_scopes: Current scope paths in the memory store.
         existing_categories: Current categories in use.
         llm: The LLM instance to use.
+        memory_prompt: Optional per-step prompt strings.
 
     Returns:
         MemoryAnalysis with suggested_scope, categories, importance, extracted_metadata.
     """
-    user = _get_prompt("save_user").format(
+    user = _memory_prompt_line(memory_prompt, "save_user").format(
         content=content,
         existing_scopes=existing_scopes or ["/"],
         existing_categories=existing_categories or [],
     )
     messages = [
-        {"role": "system", "content": _get_prompt("save_system")},
+        {
+            "role": "system",
+            "content": _memory_prompt_line(memory_prompt, "save_system"),
+        },
         {"role": "user", "content": user},
     ]
     try:
@@ -322,6 +342,7 @@ def analyze_for_consolidation(
     new_content: str,
     existing_records: list[MemoryRecord],
     llm: Any,
+    memory_prompt: MemoryPromptConfig | None = None,
 ) -> ConsolidationPlan:
     """Decide insert/update/delete for a single memory against similar existing records.
 
@@ -332,6 +353,7 @@ def analyze_for_consolidation(
         new_content: The new content to store.
         existing_records: Existing records that are semantically similar.
         llm: The LLM instance to use.
+        memory_prompt: Optional per-step prompt strings.
 
     Returns:
         ConsolidationPlan with actions per record and whether to insert the new content.
@@ -345,12 +367,15 @@ def analyze_for_consolidation(
             f"- id={r.id} | scope={r.scope} | importance={r.importance:.2f} | created={created}\n"
             f"  content: {r.content[:200]}{'...' if len(r.content) > 200 else ''}"
         )
-    user = _get_prompt("consolidation_user").format(
+    user = _memory_prompt_line(memory_prompt, "consolidation_user").format(
         new_content=new_content,
         records_summary="\n\n".join(records_lines),
     )
     messages = [
-        {"role": "system", "content": _get_prompt("consolidation_system")},
+        {
+            "role": "system",
+            "content": _memory_prompt_line(memory_prompt, "consolidation_system"),
+        },
         {"role": "user", "content": user},
     ]
     try:
diff --git a/lib/crewai/src/crewai/memory/encoding_flow.py b/lib/crewai/src/crewai/memory/encoding_flow.py
index acd025d55..baf94c9c9 100644
--- a/lib/crewai/src/crewai/memory/encoding_flow.py
+++ b/lib/crewai/src/crewai/memory/encoding_flow.py
@@ -314,6 +314,7 @@ class EncodingFlow(Flow[EncodingState]):
                         item.content,
                         list(item.similar_records),
                         self._llm,
+                        self._config.memory_prompt,
                     )
                 elif not fields_provided and not has_similar:
                     # Group C: field resolution only
@@ -324,6 +325,7 @@ class EncodingFlow(Flow[EncodingState]):
                         existing_scopes,
                         existing_categories,
                         self._llm,
+                        self._config.memory_prompt,
                     )
                 else:
                     # Group D: both in parallel
@@ -334,6 +336,7 @@ class EncodingFlow(Flow[EncodingState]):
                         existing_scopes,
                         existing_categories,
                         self._llm,
+                        self._config.memory_prompt,
                     )
                     consol_futures[i] = pool.submit(
                         contextvars.copy_context().run,
@@ -341,6 +344,7 @@ class EncodingFlow(Flow[EncodingState]):
                         item.content,
                         list(item.similar_records),
                         self._llm,
+                        self._config.memory_prompt,
                     )
 
             # Collect field-resolution results
diff --git a/lib/crewai/src/crewai/memory/recall_flow.py b/lib/crewai/src/crewai/memory/recall_flow.py
index 3a058f27b..3bc0b1a54 100644
--- a/lib/crewai/src/crewai/memory/recall_flow.py
+++ b/lib/crewai/src/crewai/memory/recall_flow.py
@@ -227,6 +227,7 @@ class RecallFlow(Flow[RecallState]):
                 available,
                 scope_info,
                 self._llm,
+                self._config.memory_prompt,
             )
             self.state.query_analysis = analysis
 
diff --git a/lib/crewai/src/crewai/memory/types.py b/lib/crewai/src/crewai/memory/types.py
index e787b569d..174ca2bf1 100644
--- a/lib/crewai/src/crewai/memory/types.py
+++ b/lib/crewai/src/crewai/memory/types.py
@@ -6,7 +6,7 @@ from datetime import datetime
 from typing import Any
 from uuid import uuid4
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 # When searching the vector store, we ask for more results than the caller
@@ -132,6 +132,126 @@ class ScopeInfo(BaseModel):
     )
 
 
+class MemoryPromptConfig(BaseModel):
+    """Configuration for memory LLM prompts (like ``PlanningConfig`` for planning).
+
+    Field names match translation keys under ``memory`` in ``translations/en.json``.
+    When set, the string replaces the bundled prompt for that step; omitted keys
+    keep the default i18n text. Templates must include the same ``str.format``
+    placeholders as the defaults (e.g. ``save_user`` uses ``{content}``,
+    ``{existing_scopes}``, ``{existing_categories}``).
+    """
+
+    model_config = ConfigDict(extra="forbid")
+
+    save_system: str | None = None
+    save_user: str | None = None
+    query_system: str | None = None
+    query_user: str | None = None
+    extract_memories_system: str | None = None
+    extract_memories_user: str | None = None
+    consolidation_system: str | None = None
+    consolidation_user: str | None = None
+
+    @classmethod
+    def for_online_people_research(cls) -> MemoryPromptConfig:
+        """Prompt profile for open-web people research (e.g. Exa).
+
+        Biases memory toward **search provenance** (queries, domains, URLs, dead
+        ends, strong sources) and away from storing fragile biographical detail
+        or speculation as high-confidence fact. Pair with ``EXASearchTool`` and
+        explicit scopes/categories in ``remember()`` when you need determinism.
+        """
+        return cls(
+            save_system=(
+                "You analyze content for a hierarchical memory used during "
+                "online people research.\n"
+                "Prioritize durable *research-process* signals over raw biographical claims:\n"
+                "- suggested_scope: use paths like /subjects/<slug>, /searches/queries, "
+                "/sources/domains when the content is about where or how you searched.\n"
+                "- categories: include tags such as search_query, result_domain, "
+                "source_url, exa_search, dead_end, preferred_source, methodology "
+                "when applicable. Use subject_profile only for well-sourced factual "
+                "snippets tied to a named URL or document - not for gossip or guesses.\n"
+                "- importance: higher (0.7-1.0) for reusable search lessons "
+                "(e.g. 'site X returned 404', 'LinkedIn useful for role Y', "
+                "'query phrasing Z worked'). Lower (0.2-0.5) for one-off personal "
+                "minutiae or unverified claims.\n"
+                "- extracted_metadata.entities: prefer domains, publication names, "
+                "and search keywords; only list people when the text explicitly "
+                "anchors a fact to a source.\n"
+                "Given the content and existing scopes and categories, output:\n"
+                "1. suggested_scope\n2. categories\n3. importance\n4. extracted_metadata"
+            ),
+            save_user=(
+                "Content to store:\n{content}\n\n"
+                "Existing scopes: {existing_scopes}\n"
+                "Existing categories: {existing_categories}\n\n"
+                "Return the analysis as structured output."
+            ),
+            query_system=(
+                "You analyze a query against memory for people-research crews.\n"
+                "Favor recalling *how and where* the team already searched:\n"
+                "- keywords: include domains, site names, query stems, and subject "
+                "handles - not only the person's name.\n"
+                "- suggested_scopes: prefer /searches, /sources, /subjects as relevant.\n"
+                "- recall_queries: produce phrases that would match memories about "
+                "Exa queries, domains tried, and conclusions about source quality.\n"
+                "- complexity: 'complex' if the question asks to synthesize many sources; "
+                "else 'simple'.\n"
+                "- time_filter: ISO date if the query implies a time window; else null.\n"
+                "Given the query and available scopes, output:\n"
+                "1. keywords\n2. suggested_scopes\n3. complexity\n4. recall_queries\n"
+                "5. time_filter"
+            ),
+            query_user=(
+                "Query: {query}\n\n"
+                "Available scopes: {available_scopes}\n"
+                "{scope_desc}\n\n"
+                "Return the analysis as structured output."
+            ),
+            extract_memories_system=(
+                "Extract discrete memory statements from research logs (tool outputs, "
+                "Exa results, agent notes).\n"
+                "HIGH priority - always capture when present:\n"
+                "- Exact or paraphrased search queries used and which engine/tool "
+                "(e.g. Exa).\n"
+                "- Domains and canonical URLs of pages opened; HTTP errors or empty results.\n"
+                "- Judgments about source usefulness (e.g. 'official bio on company site', "
+                "'social profile - verify elsewhere').\n"
+                "MEDIUM priority:\n"
+                "- Verifiable facts explicitly tied to a source in the text "
+                "(quote which source).\n"
+                "LOW priority / often skip:\n"
+                "- Speculation, reputation gossip, or detailed private life unless "
+                "the task explicitly requires it and a primary source is named.\n"
+                "Each memory: one clear sentence, no duplicate ideas.\n"
+                'Output JSON with a single key "memories" whose value is a list of strings.'
+            ),
+            extract_memories_user=(
+                "Content:\n{content}\n\n"
+                "Extract memory statements as described. Return structured output."
+            ),
+            consolidation_system=(
+                "You consolidate new research content with similar existing memories.\n"
+                "Prefer keeping separate memories when they differ by URL, domain, or "
+                "search query - even if about the same person.\n"
+                "Merge or update when the new text clearly refines the same source "
+                "or same search finding.\n"
+                "Delete only when a memory is clearly wrong and superseded.\n"
+                "Be conservative: prefer 'keep' when unsure.\n"
+                "For each existing memory: keep | update | delete. "
+                "Set insert_new when the new content adds a distinct query, URL, or "
+                "source judgment."
+            ),
+            consolidation_user=(
+                "New content to consider storing:\n{new_content}\n\n"
+                "Existing similar memories:\n{records_summary}\n\n"
+                "Return the consolidation plan as structured output."
+            ),
+        )
+
+
 class MemoryConfig(BaseModel):
     """Internal configuration for memory scoring, consolidation, and recall behavior.
 
@@ -141,6 +261,11 @@ class MemoryConfig(BaseModel):
     compute_composite_score.
     """
 
+    memory_prompt: MemoryPromptConfig | None = Field(
+        default=None,
+        description="Per-step prompt strings overriding bundled memory prompts.",
+    )
+
     # -- Composite score weights --
     # The recall composite score is:
     #   semantic_weight * similarity + recency_weight * decay + importance_weight * importance
diff --git a/lib/crewai/src/crewai/memory/unified_memory.py b/lib/crewai/src/crewai/memory/unified_memory.py
index d879bace0..bfc1eec94 100644
--- a/lib/crewai/src/crewai/memory/unified_memory.py
+++ b/lib/crewai/src/crewai/memory/unified_memory.py
@@ -9,7 +9,13 @@ import threading
 import time
 from typing import TYPE_CHECKING, Annotated, Any, Literal
 
-from pydantic import BaseModel, ConfigDict, Field, PlainValidator, PrivateAttr
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    PlainValidator,
+    PrivateAttr,
+)
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.memory_events import (
@@ -26,6 +32,7 @@ from crewai.memory.storage.backend import StorageBackend
 from crewai.memory.types import (
     MemoryConfig,
     MemoryMatch,
+    MemoryPromptConfig,
     MemoryRecord,
     ScopeInfo,
     compute_composite_score,
@@ -59,6 +66,10 @@ class Memory(BaseModel):
     Works without agent/crew. Uses LLM to infer scope, categories, importance on save.
     Uses RecallFlow for adaptive-depth recall. Supports scope/slice views and
     pluggable storage (LanceDB default).
+
+    Override LLM prompts per step via ``memory_prompt`` (same idea as
+    ``PlanningConfig.system_prompt`` / ``plan_prompt``): set only the strings you
+    need; the rest stay on bundled translations.
     """
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -135,6 +146,13 @@ class Memory(BaseModel):
             "will store memories at '/crew/research/<inferred_scope>'."
         ),
     )
+    memory_prompt: MemoryPromptConfig | None = Field(
+        default=None,
+        description=(
+            "Optional prompt strings for save, query, extract, and consolidation steps. "
+            "See MemoryPromptConfig; unset fields use translations/en.json defaults."
+        ),
+    )
 
     _config: MemoryConfig = PrivateAttr()
     _llm_instance: BaseLLM | None = PrivateAttr(default=None)
@@ -181,6 +199,7 @@ class Memory(BaseModel):
     def model_post_init(self, __context: Any) -> None:
         """Initialize runtime state from field values."""
         self._config = MemoryConfig(
+            memory_prompt=self.memory_prompt,
             recency_weight=self.recency_weight,
             semantic_weight=self.semantic_weight,
             importance_weight=self.importance_weight,
@@ -638,7 +657,9 @@ class Memory(BaseModel):
         Returns:
             List of short, self-contained memory statements.
         """
-        return extract_memories_from_content(content, self._llm)
+        return extract_memories_from_content(
+            content, self._llm, self._config.memory_prompt
+        )
 
     def recall(
         self,
diff --git a/lib/crewai/src/crewai/telemetry/telemetry.py b/lib/crewai/src/crewai/telemetry/telemetry.py
index 7809c5b4c..c93a06ff2 100644
--- a/lib/crewai/src/crewai/telemetry/telemetry.py
+++ b/lib/crewai/src/crewai/telemetry/telemetry.py
@@ -51,6 +51,7 @@ from crewai.telemetry.utils import (
     add_crew_and_task_attributes,
     add_crew_attributes,
     close_span,
+    crew_memory_span_attribute_value,
 )
 from crewai.utilities.logger_utils import suppress_warnings
 from crewai.utilities.string_utils import sanitize_tool_name
@@ -280,7 +281,11 @@ class Telemetry:
             self._add_attribute(span, "python_version", platform.python_version())
             add_crew_attributes(span, crew, self._add_attribute)
             self._add_attribute(span, "crew_process", crew.process)
-            self._add_attribute(span, "crew_memory", crew.memory)
+            self._add_attribute(
+                span,
+                "crew_memory",
+                crew_memory_span_attribute_value(crew.memory),
+            )
             self._add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
             self._add_attribute(span, "crew_number_of_agents", len(crew.agents))
 
diff --git a/lib/crewai/src/crewai/telemetry/utils.py b/lib/crewai/src/crewai/telemetry/utils.py
index c6b649a30..0e818d94d 100644
--- a/lib/crewai/src/crewai/telemetry/utils.py
+++ b/lib/crewai/src/crewai/telemetry/utils.py
@@ -16,6 +16,19 @@ if TYPE_CHECKING:
     from crewai.task import Task
 
 
+def crew_memory_span_attribute_value(memory: Any) -> bool | str:
+    """Serialize ``Crew.memory`` for OpenTelemetry span attributes.
+
+    OTLP only allows bool, str, bytes, int, float, and homogeneous sequences
+    of those types — not arbitrary objects like :class:`~crewai.memory.unified_memory.Memory`.
+    """
+    if memory is None or memory is False:
+        return False
+    if memory is True:
+        return True
+    return type(memory).__name__
+
+
 def add_agent_fingerprint_to_span(
     span: Span, agent: Any, add_attribute_fn: Callable[[Span, str, Any], None]
 ) -> None:
diff --git a/lib/crewai/tests/memory/test_unified_memory.py b/lib/crewai/tests/memory/test_unified_memory.py
index f36bf0c2b..7c7ef4345 100644
--- a/lib/crewai/tests/memory/test_unified_memory.py
+++ b/lib/crewai/tests/memory/test_unified_memory.py
@@ -664,6 +664,53 @@ def test_remember_survives_llm_failure(
     assert mem._storage.count() == 1
 
 
+# --- Per-Memory prompt config (MemoryPromptConfig) ---
+
+
+def test_memory_prompt_overrides_for_online_people_research() -> None:
+    from crewai.memory.types import MemoryPromptConfig
+
+    po = MemoryPromptConfig.for_online_people_research()
+    assert po.save_system and "search_query" in po.save_system
+    assert po.extract_memories_system and "Exa" in po.extract_memories_system
+    assert po.query_system and "recall_queries" in po.query_system
+
+
+def test_memory_prompt_overrides_save_system_used_in_analyze(tmp_path: Path) -> None:
+    from crewai.memory.analyze import analyze_for_save
+    from crewai.memory.types import MemoryPromptConfig
+    from crewai.memory.unified_memory import Memory
+
+    custom_system = "CUSTOM_SAVE_SYSTEM_OVERRIDE"
+    llm = MagicMock()
+    llm.supports_function_calling.return_value = False
+    llm.call.return_value = (
+        '{"suggested_scope": "/", "categories": [], "importance": 0.5, '
+        '"extracted_metadata": {"entities": [], "dates": [], "topics": []}}'
+    )
+
+    mem = Memory(
+        storage=str(tmp_path / "ov_db"),
+        embedder=MagicMock(),
+        llm=llm,
+        memory_prompt=MemoryPromptConfig(save_system=custom_system),
+    )
+    assert mem._config.memory_prompt is not None
+    assert mem._config.memory_prompt.save_system == custom_system
+
+    analyze_for_save(
+        "hello",
+        existing_scopes=["/"],
+        existing_categories=[],
+        llm=llm,
+        memory_prompt=mem._config.memory_prompt,
+    )
+    call_args = llm.call.call_args
+    messages = call_args[0][0]
+    assert messages[0]["role"] == "system"
+    assert messages[0]["content"] == custom_system
+
+
 # --- Agent.kickoff() memory integration ---
 
 
diff --git a/lib/crewai/tests/telemetry/test_telemetry.py b/lib/crewai/tests/telemetry/test_telemetry.py
index d0564982d..c72f0fd44 100644
--- a/lib/crewai/tests/telemetry/test_telemetry.py
+++ b/lib/crewai/tests/telemetry/test_telemetry.py
@@ -3,8 +3,9 @@ import threading
 from unittest.mock import patch
 
 import pytest
-from crewai import Agent, Crew, Task
+from crewai import Agent, Crew, Memory, Task
 from crewai.telemetry import Telemetry
+from crewai.telemetry.utils import crew_memory_span_attribute_value
 from opentelemetry import trace
 
 
@@ -159,3 +160,20 @@ def test_no_signal_handler_traceback_in_non_main_thread():
     mock_holder["logger"].debug.assert_any_call(
         "Skipping signal handler registration: not running in main thread"
     )
+
+
+@pytest.mark.parametrize(
+    ("memory", "expected"),
+    [
+        (False, False),
+        (None, False),
+        (True, True),
+    ],
+)
+def test_crew_memory_span_attribute_value_primitives(memory, expected):
+    assert crew_memory_span_attribute_value(memory) is expected
+
+
+def test_crew_memory_span_attribute_value_memory_instance():
+    """Custom Memory instances must become a primitive string for OTLP."""
+    assert crew_memory_span_attribute_value(Memory()) == "Memory"